Targeted saliva multi-omics is a reliable, non-invasive method to capture physiological stress and recovery

Determining physiological stress at high resolution is crucial across diverse settings to enable informed decision-making in the context of health and disease. Saliva-based targeted multi-omics testing provides a powerful, non-invasive method to quantify physiological stress and circadian dynamics at high-frequency. In a laboratory crossover trial with 24-hour sampling comprising 413 saliva samples, we demonstrate high analytical reliability, distinct molecular individuality, and robust acute and delayed responses to physical exercise across proteins, metabolites, and lipids. Moreover, we present the most comprehensive existing dataset describing 24-hour molecular kinetics across these three omics layers. Leveraging this controlled setting, we applied machine learning to single-timepoint saliva samples to accurately predict recent physical exercise both immediately after and 24 hours later. Next, we translated this analytical framework to a real-world longitudinal setting of elite football players monitored over 16 months, comprising over 12,000 saliva samples. Despite increased biological and contextual variability, the model retained robust discrimination between exercise and rest on the following day. Based on prediction probabilities, we introduce a saliva-based internal strain metric, that captures internal load and can be harnessed to monitor physical exercise and recovery. Model robustness was further supported through out-of-sample validation using previously unseen observations. Our findings demonstrate that saliva-based targeted multi-omics reliably captures physical exercise and recovery states in both laboratory and real-world environments, providing a scalable framework for monitoring physical performance. This non-invasive approach holds broad potential for physiological monitoring and can serve as a blueprint for health- and disease-related contexts.